Predicting the risk of pulmonary infection in patients with chronic kidney failure : A-C2GH2S risk score-a retrospective study
© 2024. The Author(s), under exclusive licence to Springer Nature B.V..
PURPOSE: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients.
METHODS: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability.
RESULTS: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5.
CONCLUSION: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
International urology and nephrology - (2024) vom: 04. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Deng, Wenqian [VerfasserIn] |
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| Links: |
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| Themen: |
Chronic kidney failure |
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| Anmerkungen: |
Date Revised 04.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s11255-024-03953-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369266897 |
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| 100 | 1 | |a Deng, Wenqian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predicting the risk of pulmonary infection in patients with chronic kidney failure |b A-C2GH2S risk score-a retrospective study |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Nature B.V. | ||
| 520 | |a PURPOSE: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients | ||
| 520 | |a METHODS: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability | ||
| 520 | |a RESULTS: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5 | ||
| 520 | |a CONCLUSION: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Chronic kidney failure | |
| 650 | 4 | |a Model evaluation | |
| 650 | 4 | |a Prediction models | |
| 650 | 4 | |a Pulmonary infection | |
| 650 | 4 | |a Risk factors | |
| 700 | 1 | |a Liu, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Qianhui |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jingwen |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Wenwen |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Jiangan |e verfasserin |4 aut | |
| 700 | 1 | |a Weng, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zhiyi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Chan |e verfasserin |4 aut | |
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